New products hit the market faster than businesses can consume them, forcing IT executives and business managers to set priorities. If policies are too rigid, competitiveness may suffer. If policies are overly agile, precious IT resources may be spent on the wrong solutions. This article reveals how product life cycles and adoption cycles differ and what IT executives have learned about striking the right balance in their organizations.

Even the most affluent companies have limited budgets so they have to set priorities, driven by business goals and competitive concerns. Some organizations allocate a certain percentage of their budget to experimentation; others wait for the market to choose the winners. Both approaches are geared to manage risks whether the risk is perceived as staying ahead of the competition or avoiding the potential pitfalls of early-stage technology adoption.

“Trailblazers prefer to take a path no one else has taken before,” said the former CIO of a major office supply retailer. “Some of that leads nowhere. but when one of them leads to the Promised Land, everyone says, ‘Oh my God, how did you do that?’"

Big data is a good example. The office supply retailer formally planned to adopt Hadoop, with the goal of combining and collectively mining structured and unstructured data. From a marketing perspective, the company wanted to understand customer sentiment, customer behavior, and buying patterns in greater detail. The business goals were to reduce costs and increase ROI by optimizing product promotions, placement, and pricing. But it didn’t happen immediately.

The team evaluated Hadoop and decided how they were going to integrate it with their existing data infrastructure, but it would be another year or more before Hadoop would be added to the mix. The first step was to master the structured data using existing systems and then integrate Hadoop.

Meanwhile, an alternative payments company was getting ready to expand its Hadoop clusters. Its data systems, which included several data management platforms, were coupled with machine learning and a team of data scientists to improve the velocity and strategic value of the data-driven insights. The goal was to get real-time insight into customer buying behavior within and across channels. Armed with such knowledge, the company would be able to offer promotions in context on-the-fly.

Part of the magic was a willingness to experiment with new technologies. It was not enough to get answers to existing questions. To innovate continuously, the business felt it necessary to identify critical questions that had not yet been conceived. “If there is something that we really need to adopt to understand the right question we do it,” said one of the data scientists. “Certainly, budget is an issue but it’s not top of mind.”

Perception is Everything

Enterprise investments are driven by a few fundamental issues that are not mutually-exclusive:

Need

Budget

Competitiveness

Timing

What already exists

Assuming the need is established, budget almost always comes into play. If budget hasn’t been allocated, the project visionaries seek a new source of funds or re-prioritize existing line items. They may consider top-line costs and total cost of ownership, but opportunity cost may be overlooked – which brings me to my next point.

The competitiveness question has two sides: Will the product help make my organization more competitive; and if so, how? Will the absence of the product impede our ability to compete and if so why? Companies that pride themselves on leadership through innovation are more inclined to adopt new technology sooner than their counterparts.

Then there is timing. The alternative payment company adopted an early version of Hadoop which was very difficult to use but it also gave the organization early-stage insight into what was possible to accomplish. The office supply retailer delayed its Hadoop adoption because it wanted to master its existing systems first. One benefit of delayed adoption in their case was that the open source community was working hard to rectify some early-stage Hadoop limitations (and resulting frustrations).

There isn’t a single right answer. Each approach served its respective company well given its different business model, culture, and business priorities.

Balancing the Speed of Innovation and the Speed of Adoption

Business software vendors know that the right mix of drivers need to be present for customers to buy, although some interesting adjustments better align the velocity at which new solutions are available and the ability of organizations to adopt them.

For example, software providers are moving from packaged, on-premise solutions to cloud-based solutions. While many companies still offer both (for now), the cloud versions provide customers with greater flexibility while the vendors benefit from lower packaging and delivery overhead and in some cases higher ROI.

Without packaged software, vendors no long need 3D package design, packaging, fulfillment warehouses, and shipping. Vendors merely place a product logo or a product picture on a website next to a download button and a price which results in faster, cheaper product delivery. On the flip side, customers can often purchase “modules” instead of large, expensive all-in-one products, and they may be able to pay only for the use of the software (use-based pricing) rather than buying a license outright. What’s more, software maintenance and upgrades occur automatically and transparently: No more patch and update installs on individual servers.

Meanwhile, hardware has become more commoditized and more dependent on software. In a lot of cases, the software can be updated many times before it exceeds the capability of the hardware, which extends the hardware’s life. The trend can be seen in everything from cell phones to telecommunication infrastructure equipment.

“Hardware changes more slowly than software,” said David Elfanbaum, co-founder of Asynchrony, a software development consulting firm. “In the mobile phone market, new hardware releases come out about every six months but a phone from three years ago is still usable. The software changes are more incremental and they can often be more dramatic.”

Bottom Line

While innovation continues to accelerate and new methods of product delivery help bridge the gap between product introductions and product adoptions, there will always be a delta between the two. When a technology is disruptive enough – like Hadoop or smartphones or even Twitter – enterprises may find themselves affected, whether it’s adopting the technology for competitive reasons or because users are changing the rules of enterprise computing.